By Hassan Aït-Kaci, Yutaka Sasaki (auth.), Luc De Raedt, Peter Flach (eds.)
This ebook constitutes the refereed lawsuits of the twelfth eu convention on desktop studying, ECML 2001, held in Freiburg, Germany, in September 2001.
The 50 revised complete papers offered including 4 invited contributions have been conscientiously reviewed and chosen from a complete of a hundred and forty submissions. one of the subject matters coated are classifier structures, naive-Bayes type, rule studying, selection tree-based category, internet mining, equation discovery, inductive good judgment programming, textual content categorization, agent studying, backpropagation, reinforcement studying, series prediction, sequential judgements, class studying, sampling, and semi-supervised learning.
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Extra info for Machine Learning: ECML 2001: 12th European Conference on Machine Learning Freiburg, Germany, September 5–7, 2001 Proceedings
In I. Smith and B. Saltings, editors, Advances in Case-based reasoning, number 1168 in Lecture Notes in Artificial Intelligence, pages 324–338. Springer-Verlag, 1996.  Enric Plaza. Cases as terms: A feature term approach to the structured representation of cases. In M. Veloso and A. Aamodt, editors, Case-Based Reasoning, ICCBR-95, number 1010 in Lecture Notes in Artificial Intelligence, pages 265–276. Springer-Verlag, 1995.  B. L. Richards and R. J. Mooney. Learning relations by pathfinding.
25–36, 2001. c Springer-Verlag Berlin Heidelberg 2001 26 H. Bensusan and A. Kalousis the performance of a classifier and the properties of the domain. In this sense it provides no principled basis for an analysis of what lies behind successful performance estimations. Meta-learning is the endeavour to learn something about the expected performance of a classifier from previous applications. In any scenario, it depends heavily on the way we choose to characterize the datasets. Meta-learning has often concentrated on predicting whether a classifier is suitable for a dataset  and on selection of the best option from a pool of classifiers[1,13].
Tracts in theoretical Computer Science. Cambridge University Press, Cambridge, UK, 1992.  W. Emde and D. Wettschereck. Relational instance based learning. In Lorenza Saitta, editor, Machine Learning - Proceedings 13th ICML, pages 122 – 130. Morgan Kaufmann Publishers, 1996.  Tamas Horvath, Stefan Wrobel, and Uta Bohnebeck. Relational instance-based learning with lists and terms. Machine Learning, 43(1):53–80, 2001.  Ramon L´ opez de M´ antaras. A distance-based attribute selection measure for decision tree induction.